A Matter of Life and Death: Validating, Qualifying and Documenting Models for Simulating Flow-Related Accident Scenarios in the Process Industry
Skjold, T.
Pedersen, H.
Bernard, L.
Middha, P.
Narasimhamurthy, V.D.
Landvik, T.
Lea, T.
Pesch, L.
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How to Cite

Skjold T., Pedersen H., Bernard L., Middha P., Narasimhamurthy V., Landvik T., Lea T., Pesch L., 2013, A Matter of Life and Death: Validating, Qualifying and Documenting Models for Simulating Flow-Related Accident Scenarios in the Process Industry, Chemical Engineering Transactions, 31, 187-192.
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Abstract

This paper describes an integrated approach for validating, qualifying and documenting numerical models for simulating complex systems. Although the example used to illustrate the process entails simulations of accident scenarios in the petroleum and process industries by means of computational fluid dynamics (CFD), the methodology is not restricted to any particular model or system. CFD tools are applicable to various aspects of societal safety, including transportation, storage and use of various energy carriers, as well as malicious attacks involving toxic gas or condensed explosives. The approach adopted involves a continuous process where relevant validation cases are classified according to the physical phenomena involved, and prioritized based on parameters such as relevance for typical applications of the model system, measurement quality and repeatability, availability of data, spatial scale, materials or substances used, etc. A model evaluation protocol (MEP) provides guidelines for prioritizing the various validation cases, and for evaluating the simulation results. Statistical methods and visualization techniques are employed for describing the validation range and the associated uncertainties of the model system. Use of the methodology is illustrated for a typical application of the commercial CFD tool FLACS: large-scale gas explosions in congested geometries. The results highlight some of the inherent challenges associated with the interpretation of results from large-scale experiments, and demonstrate how such challenges can be addressed during the model evaluation process. The methodology can be extended to include sensitivity studies and advanced optimization schemes for key model parameters.
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